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Plant and machine operators, and assemblers · code 8157

Laundry machine operators

The machines do the washing. Sorting, stain-spotting and folding still need people.

AI barely touches any of the nine tasks, from loading washers and dryers and getting stains out to small repairs and packing orders.

What they actually do (and what else they get called)

Laundry machine operators operate laundry, dry-cleaning, pressing and fabric treatment machines in laundries and dry-cleaning establishments. Tasks include - (a) sorting articles for cleaning according to the type, colour, fabric and cleaning treatment required; (b) placing sorted articles into receptacles and onto conveyor belts for moving to repair and cleaning areas; (c) checking and removing stains from garments, replacing buttons and making minor repairs; (d) loading and unloading washing machines, driers and extractors; (e) adding cleaning agents and starches to articles; (f) smoothing articles and guiding them through cleaning and pressing machines; (g) stopping and starting machines to untangle, straighten and remove articles; (h) placing articles on shelves and hanging articles for delivery and collection; (i) packaging articles and preparing orders for despatch. Examples of the occupations classified here: - Dry-cleaning machine operator - Laundry machine operator - Pressing machine operator (laundry) Some related occupations classified elsewhere: - Hand launderer - 9121 - Hand presser - 9121

  • laundry worker
  • laundry workers supervisor

Job titles from the EU's ESCO list. Search for any of them on the jobs page and you'll end up here.

84 / 100

Strongly holds up against AI

Most of this job still needs an actual human. AI would mostly be making the tea.

Not the odds you keep your job. It's how much of the everyday work still needs a person, out of 100.

← Robot's turf Human's turf →

The ILO's official verdict: Not Exposed

82 of 427 for holding up against AI

Why this score?

The ILO rated each of this job's 9 tasks for how much of it AI could do. They average 15.6 out of 100, so it holds up at 100 − 15.6 = 84.4.

That's the whole sum. No black box, no secret sauce: you can check it against the ILO's study. ILO group “Not Exposed”: None of this job's tasks scored high enough to count.

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.8-observed-use

Pinch of salt: this number is probably too rosy

It only asks what AI software could do, and this job is 58 out of 100 hands-on. A chatbot can't do that part. A robot might, one day, and nobody measures how close that is.

Man v Bot · compare

Who holds up better?

Laundry machine operators 84

v

Go on, try your partner's job. Or your sibling's. We won't tell.

Man v Bot

Where the bot helps, and where the human wins

Bot

What AI takes

The routine stuff. You may not miss it.

  • Order tracking and despatch.
  • Machine settings and schedules.
  • Tagging items with barcodes or RFID.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

  • Sorting by fabric, colour and treatment.
  • Spotting stains and needed repairs.
  • Untangling and handling awkward items.

Both

What's arriving

New work that only exists because the AI does.

  • Working with folding robots.
  • Tracked linen services for hotels and hospitals.
  • Lower-energy washing.

Written by a human, from the ILO's task ratings below. Yes, we see the irony. Read it as one person's take.

Meet your two rivals

They're not the same machine. AI software can only reach the parts of a job done on a screen or in words. A chatbot can't climb a ladder. A robot might, one day, if it doesn't fall off.

The AI

Lives in a screen. Never had to fix a boiler.

Threat level: Low

19 could do without you

It could take over very little of the work on its own. It could help with 43 of 100.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Some

58 how hands-on

A fair part of this job is hands-on, so robots would be needed for that part, not just software.

So could a robot do the physical part?

Less fine handwork and fewer cramped spaces than a typical hands-on job. That clears one hurdle for machines. It doesn't mean machines are doing this work yet.

Robots hate fiddly work in awkward spaces. On that measure, this job scores 32 out of 100. The higher it is, the longer the robot waits.

58 How
hands-on
32 How fiddly
and cramped

Kind of hands-on work Hands-on, less fiddly work

The small print

They aren't a robot score, and nothing on this page is. Nobody measures how far robots have got well enough to put a number on it. These are the closest thing we have: how hands-on the job is, and how much fiddly, cramped work it takes, which is what Frey and Osborne found hardest for machines.

From O*NET, matched to one US job .

finger skill 41.1 · hand skill 41.1 · cramped spaces 14.3 · averaged · rule: hands-on 50+ · fiddly under 39.4

estimated O*NET 31.0 BLS occupational crosswalk chain ISCO-08 to 2010 SOC (Aug 2012, rev. Jun 2015); 2010 to 2018 SOC (Nov 2017); O*NET-SOC 2019 taxonomy data 0.8-observed-use measures chosen by Frey & Osborne (2017) · the grouping is a sorting, not a measurement

The job, task by task

Every job is really a bundle of tasks. Here's this one unpacked, with how much of each AI could do, out of 100. The score is the average, and averages can hide a lot.

AI's favourite task here scores 23, against an average of 16. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

  1. 23

    Packaging articles and preparing orders for despatch.

    Very Low
  2. 17

    Placing articles on shelves and hanging articles for delivery and collection

    Very Low
  3. 16

    Smoothing articles and guiding them through cleaning and pressing machines

    Very Low
  4. 15

    Adding cleaning agents and starches to articles

    Very Low
  5. 15

    Stopping and starting machines to untangle, straighten and remove articles

    Very Low
  6. 14

    Sorting articles for cleaning according to the type, colour, fabric and cleaning treatment required

    Very Low
  7. 14

    Placing sorted articles into receptacles and onto conveyor belts for moving to repair and cleaning areas

    Very Low
  8. 14

    Checking and removing stains from garments, replacing buttons and making minor repairs

    Very Low
  9. 14

    Loading and unloading washing machines, driers and extractors

    Very Low

9 tasks · the High to Very Low labels are the ILO's

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.8-observed-use

Sidekick or replacement?

A tool that makes you faster is a very different thing from one that does the task while you watch. Jobs and Skills Australia measured the two separately, which is handy.

AI helps you
43
AI does it for you
19

Sidekick wins, by 24 points.

How good a match is this?

One Australian job matches, but only partly: not everyone in it does this job. Treat the numbers as a rough guide.

estimated Our Gen AI Transition (Gen AI Capacity Study) September 2025 detailed data release data 0.8-observed-use matched to Australian jobs 8115

So, should laundry workers be worried?

Short answer: not really. Put the kettle on.

No. Every task rates Very Low; the most exposed is packing orders for despatch. Industrial laundries automate washing and some folding, but sorting, repairs and awkward items stay hands-on.

Worth remembering: we score tasks, not jobs. When AI takes on a task, the job usually reshuffles around it rather than vanishing.

Where this job could lead

None of the new jobs we've written up start from here yet. Could be you're the first. See the jobs AI is creating →

How are the neighbours doing?

There are 39 jobs in the “Plant and machine operators, and assemblers” family, averaging 80. This one is above the family average.

Pit this against any other job →